Main results

First, I report the main results of the wage equations followed by a presentation of the employment equations. The main results are consistent with my hypothesis, suggesting that public R&D is rather weakly associated with researchers’ wages̅̅̅̅̅̅̅̅, while employment exhibits a strong association with public R&D.

7.1.1. Wage equations

Table 9 shows the results of the three regressions that explore the relationship between researchers’ wages̅̅̅̅̅̅̅̅ and public R&D spending. Column 1 shows the relationship between contemporaneous public R&D spending and wages̅̅̅̅̅̅̅̅, while column 2 and 3 show a one-year and two-year lag of public R&D spending impact on current wages̅̅̅̅̅̅̅̅. In all specifications, the coefficients of interest are positive and statistically significant. The r-square, ranging between 92.5% and 96.1%, are satisfactorily high.

Table 9 – Wage equations

Dependent variable: log𝐰𝐚𝐠𝐞̅̅̅̅̅̅̅̅ (1) (2) (3)

LSDV LSDV LSDV

logPUBRD 0.0832***

(0.02410)

1 year lag of logPUBRD 0.0779***

(0.02602)

2 year lag of logPUBRD 0.0575**

(0.02329)

Occupation and Time dummies yes yes yes

Observations 149 131 113

R-square 0.925 0.941 0.961

Years 2007-2015 2007-2015 2007-2015

Note: Significance levels: *** p<1%, ** p<5%, * p<10%.

Standard errors are clustered at the occupation level and are robust to heteroscedasticity. Standard errors are reported in the brackets. Variables, log𝑤𝑎𝑔𝑒̅̅̅̅̅̅̅ and logPUBRD, have been deflated using CPI and PPI indices. All regressions are based on least square dummy variable models and include both occupation and time fixed effects. Regression (1) is based on current PUBRD spending.

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Regression (2) includes a one-year lagged PUBRD spending independent variable. Regression (3) includes a two-year lagged PUBRD spending independent variable. Columns 2 and 3 have fewer observations due to the lagged variables.

In the baseline LSDV regression, column 1 in Table 9, I find evidence of a positive relationship between current public R&D spending and researchers’ wages̅̅̅̅̅̅̅̅. The magnitude effect is however rather small. A 10% increase in current public R&D spending is associated with a 0.83% increase in researchers’ wages̅̅̅̅̅̅̅̅ and is statistically significant at the one percent level.

In comparison, Goolsbee (1998) finds an increase of 2.3% in scientists’ and engineers’ wages for a 10% increase in federal R&D. Whereas Marey and Borghans (2000) find aggregate average wages to increase by 5.2% as a response to a 10% increase in total R&D spending.

When I allow wages̅̅̅̅̅̅̅̅ to adjust to public R&D spending by including lagged variables, the magnitude effect decreases. This finding is at odds with the expectation that it may take some time for the wages to adjust to changes in public R&D spending. Column 2 shows that a 10%

increase in previous year’s public R&D spending is associated with a 0.78% increase in current researchers’ wages̅̅̅̅̅̅̅̅, which is statistically significant at the five percent. The estimate magnitude drops even further when I include a two-year lag of PUBRD spending. Column 3 shows that a 10% increase in public R&D spending from two-years ago is associated with a 0.58% increase in current researchers’ wages̅̅̅̅̅̅̅̅. The relationship is statistically significant at the five percent level. At first glance, the lower magnitudes contradicts my expectation and previous literature. However, at a closer look, Table 9 shows that a one-year lag and two-year lag of public R&D expenditures yield higher r-squares, which suggest that regression 2 and 3 fit the model better. In fact, when including a two-year lag, the PUBRD explains 96% of the variation in wages̅̅̅̅̅̅̅̅ compared to the concurrent relationship in which the coefficient of determination is 92.5%. The trend of decreasing coefficients of interest and increasing r-square the longer the lag, is possible explained by the compositional bias. If higher public R&D expenditures two years ago induce private companies to hire more researchers today, they will primarily opt for recent graduates who are looking for jobs. In such case, researchers’

wages

̅̅̅̅̅̅̅̅ will decrease as recent graduates are usually paid less. While this is a possible explanation, I cannot exclude that the attenuation is due to the reduction in sample sizes when one- and two-year lags are included in the specifications.

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Table 10 presents the six regressions that explore the relationship between employment and public R&D spending. The output in column 1 to 3 shows the relationship between private employment and current, one-year lag and two-year lag of public R&D spending. Column 3-6 presents these relationships for the total employment. All specifications exhibit a strong positive magnitude. However, only output 3 is statistically significant. All specifications have satisfactory high r-square that range between 92.5% and 96.1%.

Table 10 – Employment equations

Private employment Total employment

Dependent variable:

logEmployment (1) (2) (3) (4) (5) (6)

LSDV LSDV LSDV LSDV LSDV LSDV

logPUBRD 0.6391 0.4471

(0.37948) (0.3805)

1 year lag of logPUBRD

0.6315 (0.42473)

0.4289 (0.36386)

2 year lag of logPUBRD

0.80**

(0.35527)

0.4424 (0.35046)

Occupation and

Time dummies yes yes yes

yes yes yes

Observations 149 131 113 149 131 113

R-square 0.943 0.948 0.973 0.925 0.936 0.954

Years 2007-2015 2007-2015 2007-2015 2007-2015 2007-2015 2007-2015 Note: Significance levels: *** p<1%, ** p<5%, * p<10%.

Standard errors are clustered at the occupation level and are robust to heteroscedasticity. Standard errors are reported in the brackets.

LogPUBRD, has been deflated using the PPI index. All regressions are based on least square dummy variable models and include both occupation and time fixed effects. Regressions (1)-(3) are based on private employment, and regressions (4)-(6) are based on total employment. Regression (1) and (4) show the current relationships. Regressions (2) and (5) include a one-year lag of PUBRD.

Regressions (3) and (6) include a two-year lag of PUBRD.

In the baseline regression, column 1 in Table 10, I find evidence of a strong positive relationship between current public R&D spending and private employment. A 10% increase in current PUBRD spending is associated with a 6.4% increase in private employment in the short run. When I include a one-year lag, in column 2, the magnitude remains approximately the same. A 10% increase in previous year’s PUBRD spending is associated with a 6.3%

increase in private employment. Both parameter estimates are however statistically insignificant. Once I allow private employment to adjust to public R&D spending by including

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a two-year lag, in column 3, the relationship turns significant at the 5% level. Furthermore, the magnitude effect increases considerably. A 10% increase in public R&D spending from two-years ago is associated with a strong increase in private employment of 8%. This finding is in line with the literature, suggesting that public R&D expenditures may take years to materialise for private employment.

The elasticity of public R&D spending of total employment, column 4 to 6, is less responsive compared to private employment. For a 10% increase in current public R&D spending, total employment increases with 4.5%. In contrast to my expectations, the magnitude effects remains approximately the same when I include lags to account for the adjustment process.

A possible explanation for the weaker effects of total employment compared to private employment, is, that I do not account for the mobility between the private and public sector.

When I consider private employment separately, a public R&D stimulus may encourage the private sector to recruit from the public sector. The total employment accounts for this inter-mobility as the only source of additional employment is through the existing stock of scientists and engineers. The elasticity of approximately 0.45 throughout reg ressions (4)-(6) thus suggests a medium effect. The medium short-run effects are in line with the findings of Marey and Borghans (2000) who find similar results. However, my findings differ significantly from those of Goolsbee (1998), who find an impact close to zero.

7.1.3. Joint interpretation

The wage equations and employment equations jointly confirm my initial hypothesis of a relative weaker wage effect compared to employment. Considering the basic model 4.1 derived in section 4 Theoretical foundation, E = w * L, the results indicate that public R&D, E, is primarily caused by growth in L, employment, rather than w, wages̅̅̅̅̅̅̅̅, which is consistent with an elastic supply curve.

Before providing possible factors that may explain these findings, I conduct a number of robustness checks to ensure that my main results are not driven by the construction of this study.